Malicious Detection and Prediction using Machine learning
Himanshi Sonparote1, Parikshit Arekar2, Roshani Madankar3, Shreyas Gosavi4, Rasika Badre5
1Student, Computer Science & Engineering Department, PRMIT&R, Badnera
2Student, Computer Science & Engineering Department, PRMIT&R, Badnera
3Student, Computer Science & Engineering Department, PRMIT&R, Badnera
4Student, Computer Science & Engineering Department, PRMIT&R, Badnera
5Assistant Professor, Computer Science & Engineering Department, PRMIT&R, Badnera.
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In the domain of computer security, there is now considerable research into machine learning-based malware detection and prediction. Algorithms for machine learning have showed promise. outcomes in the identification and prediction of harmful software existence in computer systems. The goal of this strategy is to provide precise and effective tools for identifying and avoiding malware attacks. The term "malware" describes harmful software, such as viruses, worms, Trojan horses, ransomware, and spyware, that is intended to harm computer systems. Attacks by malware can cause large monetary losses, privacy breaches, and reputational harm. Therefore, it is essential to provide reliable and effective techniques for identifying and avoiding malware attacks.
Algorithms for machine learning have demonstrated promise in the detection and forecasting of dangerous software. Computer learning is a branch of artificial intelligence that deals with teaching algorithms to discover patterns in data and generate predictions using those patterns. The method includes giving a machine learning algorithm a sizable dataset of known malware samples, which subsequently learns to recognize common patterns and traits associated with malware. The trained algorithm may then be employed to determine if new and unexplored data contains malware.
Key Words: Technological innovation; manipulation thread; KNN; SVM; DT; Cyber security; Cyber-attack; suspicious activity; Cyber threat.
